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...the population as a whole. • Compute entropy (Shannon or Rényi—please justify your choice) across sliding and tumbling windows so we can compare immediate behaviour to longer-term baselines. • Raise an alert when an entropy shift exceeds a configurable threshold, returning the supporting metrics and the related raw events. I expect well-structured, runnable code—Python with pandas/NumPy/SciPy is typical, though another language is fine if it delivers the same reproducible results—along with a concise README that shows how to install dependencies, feed sample logs, and interpret the output. Success is measured by: • Clean execution on my sample dataset (≈1 GB of mixed user activity). • Alerts that capture injected anom...

I’m refining a series of ...stability and interpret the Hessian output for standard-error estimation. • Suggest parameter-regularisation strategies available directly in SciPy or via light dependencies I can bolt on. • Join one or two live screen-share sessions (30-45 min each) so we can step through residual plots, goodness-of-fit tests, and any edge-case handling. All work will happen in a clean Python 3.11 environment with NumPy, SciPy 1.11, Pandas, and Matplotlib already installed, so no need for heavy-weight ML frameworks. Deliverables are the commented revisions to my script plus a concise summary of the changes and reasoning. If you’ve previously tuned logistic models in SciPy (not just scikit-learn) and enjoy explaining the “wh...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

...analysis of that periodic state so I can identify the short-wave instability noses and the curious stability islands created by the substrate’s geometry. All governing equations and parameter ranges are ready; what I still need is a clean, reproducible Floquet framework that extracts the multipliers for each wavenumber and returns clear stability maps. You are free to work in MATLAB, Python (NumPy/SciPy), or Julia as long as the code is well commented and numerically robust—spectral collocation or a high-order finite-difference approach is fine as long as convergence is demonstrated. Deliverables • A runnable script or notebook that assembles the monodromy matrix for the periodic WIBL coefficients and computes the Floquet multipliers across user-defined p...

...that depend on the substrate’s steepness. What I still need is a rigorous, publication-ready consolidation of the theory together with reproducible numerical evidence. Your job is to extend and verify the existing derivations, implement a robust time-dependent solver, and map out finite-amplitude travelling solutions across a representative parameter space. If you prefer MATLAB, Python (NumPy/SciPy), or a spectral‐element COMSOL workflow, that is fine, provided the final code runs out of the box and reproduces the key figures. Deliverables – the project is complete when you hand over • a clean derivation that connects the long-wave model to the full viscoelastic Navier–Stokes equations, • stability curves and nonlinear bifurcation diagrams that c...

...version, my research notes, and the raw data set. The text follows Chicago style, so every edit—from footnotes to bibliography—has to respect that convention. Here is what I need from you: • Edit and streamline the entire manuscript for clarity, logical flow, and persuasive argumentation while eliminating grammatical or stylistic errors. • Run or verify my data analysis in SPSS or Python (pandas, SciPy, or a comparable library), then weave the statistical interpretation seamlessly into the discussion section. • Ensure every citation, table, and figure aligns with Chicago guidelines and that all references are cross-checked for accuracy. Acceptance criteria • Clean, publication-ready manuscript (tracked-changes + final version) in .docx for...

...clean the dataset, handle missing or noisy readings, and then produce descriptive statistics (mean, median, variance, standard deviation, covariance), visualisations that highlight key insights (time-series plots, histograms, perhaps a correlation heat map), and a concise written interpretation of what the numbers show. I’m comfortable with Python, so a Jupyter Notebook built with pandas, NumPy, SciPy and Matplotlib or Seaborn is ideal. If you prefer R or MATLAB, that’s fine too—just keep the code readable and reproducible. Deliverables • Cleaned data file in the same structure as the original • Well-commented analysis notebook or script • Exported graphs (PNG or PDF) • Short report (1-2 pages) summarising findings and highlighting ...

...analysis, time-frequency techniques—whatever best extracts degradation features), train a predictive model, then expose Remaining Useful Life or probability-of-failure metrics in an intuitive web dashboard. Vibration data will be the only input in the first release, so your signal-processing and machine-learning choices must squeeze maximum insight from that single source. I’m open to Python (NumPy, SciPy, scikit-learn, TensorFlow) or MATLAB toolchains as long as the final product is easy for me to retrain with new runs. Deliverables • Source code with clear comments and a short setup guide • A lightweight dashboard (Streamlit, Dash, or similar) showing live health indicators, trend plots and a simple traffic-light status • A sample dataset and step-b...

...analysis, time-frequency techniques—whatever best extracts degradation features), train a predictive model, then expose Remaining Useful Life or probability-of-failure metrics in an intuitive web dashboard. Vibration data will be the only input in the first release, so your signal-processing and machine-learning choices must squeeze maximum insight from that single source. I’m open to Python (NumPy, SciPy, scikit-learn, TensorFlow) or MATLAB toolchains as long as the final product is easy for me to retrain with new runs. Deliverables • Source code with clear comments and a short setup guide • A lightweight dashboard (Streamlit, Dash, or similar) showing live health indicators, trend plots and a simple traffic-light status • A sample dataset and ...

...Validate mathematical assumptions using statistical and numerical methods - Optimize performance, stability, and signal quality Required Skills & Expertise Mathematics & Quantitative Skills - Strong background in advanced mathematics (calculus, applied mathematics, numerical methods) - Time-series analysis and market behavior modeling Programming Skills - Python (expert level): NumPy, Pandas, SciPy, backtesting frameworks - Pine Script (TradingView): custom indicators, optimization, real-time constraints Trading & Market Knowledge - Market structure, momentum, acceleration, price action - Reversal and exhaustion dynamics - Experience in quantitative or algorithmic trading is mandatory Bonus / Nice-to-Have - Artificial Intelligence / Machine Learning experience - F...

...advanced Python users who now want to master Data Science and Machine Learning. They already write clean, object-oriented code, but need structured help turning that skill into solid analytical practice. Your role is to meet them online, break down real-world datasets, and walk them through both the theory and hands-on implementation of: • Statistical analysis & data visualisation: pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly, plus the “why” behind each chart or test. • Core machine-learning algorithms: from linear and logistic regression through tree-based models, clustering, model evaluation and cross-validation using scikit-learn. Weekly rhythm I have in mind is one live workshop (60–90 min) and one code-review/drop-in clinic. I&rs...

...• Receive periodic CSV, JSON, or database exports (mainly MySQL and Postgres) • Clean and validate each dataset, flagging anomalies or gaps • Build concise exploratory analyses, then dive deeper with statistical tests or modelling when patterns emerge • Produce easy-to-read visual dashboards or slide decks that tell the story behind the numbers Preferred toolset Python (pandas, NumPy, SciPy), SQL, Excel/Google Sheets, and a modern BI platform such as Tableau, Power BI, or Looker. If you favour R or another stack and can achieve the same clarity, feel free to propose it. Deliverables 1. Cleaned dataset with documented steps 2. Analytical notebook or script ready for reruns 3. Interactive dashboard or static visual pack (PDF/PPT) presenting key fin...

...questions I want the model to answer. What I’m missing is an experienced collaborator who can stress-test the assumptions, confirm the equations make sense biologically, and help shape the discussion so it stands up to peer review. Familiarity with population genetics, evolutionary game theory, or comparative methods will be especially helpful, and the ability to use tools like R, Python (NumPy/SciPy), or Mathematica for quick simulations will make the process smoother. Ultimately, I’d like to finish with: – a polished set of equations or simulation code, – clearly interpreted results tied back to the current literature, and – concise text ready to be slotted into my manuscript draft. If you enjoy dissecting evolutionary theory and have a kna...

...actionable allocation recommendations. My focus is preskriptif analysis, so I’m not just after insights—I need an optimizer that suggests exactly how to rebalance a multi-asset portfolio for better risk-adjusted returns. The raw figures are already collected; what’s missing is the model that converts them into concrete decisions. You’re free to choose the most suitable stack—Python (Pandas, NumPy, SciPy, PyPortfolioOpt), R, or another reliable tool—as long as the results are reproducible and the code is well commented. Deliverables • A cleaned, well-structured dataset ready for modelling • A prescriptive optimisation model with parameter explanations • Recommended asset weights along with scenario stress-tests • ...

...actionable allocation recommendations. My focus is preskriptif analysis, so I’m not just after insights—I need an optimizer that suggests exactly how to rebalance a multi-asset portfolio for better risk-adjusted returns. The raw figures are already collected; what’s missing is the model that converts them into concrete decisions. You’re free to choose the most suitable stack—Python (Pandas, NumPy, SciPy, PyPortfolioOpt), R, or another reliable tool—as long as the results are reproducible and the code is well commented. Deliverables • A cleaned, well-structured dataset ready for modelling • A prescriptive optimisation model with parameter explanations • Recommended asset weights along with scenario stress-tests • ...

...questions I want the model to answer. What I’m missing is an experienced collaborator who can stress-test the assumptions, confirm the equations make sense biologically, and help shape the discussion so it stands up to peer review. Familiarity with population genetics, evolutionary game theory, or comparative methods will be especially helpful, and the ability to use tools like R, Python (NumPy/SciPy), or Mathematica for quick simulations will make the process smoother. Ultimately, I’d like to finish with: – a polished set of equations or simulation code, – clearly interpreted results tied back to the current literature, and – concise text ready to be slotted into my manuscript draft. If you enjoy dissecting evolutionary theory and have a kna...

...contain the usual noise—missing values, possible outliers, and a mix of categorical and numerical variables—so the first step will be a careful data-cleaning routine. Once the data are tidy, I want you to choose and justify the appropriate statistical tests, run them, and interpret the results in plain language that non-statisticians can follow. Please use reproducible code in either Python (pandas, SciPy, statsmodels, seaborn) or R (tidyverse, ggplot2, broom). Along with the scripts or notebooks, I expect a concise report (PDF or Word) that covers: • a description of the cleaned dataset • the assumptions checked for each test • key findings with p-values, confidence intervals, and effect sizes • clear visualisations that highlight the most...

...a set of physics-journal datasets and several higher-order theoretical equations that I need handled entirely in Python. The job is two-fold: first, deliver working code that plots line graphs, bar charts, and scatter plots drawn straight from the papers; second, build numerical routines that solve my higher-order equations with solid, peer-review-ready accuracy. I expect the core stack—NumPy, SciPy, and Matplotlib—to be used, and I’m happy for you to introduce any additional scientific libraries if they streamline the workflow or boost performance. All scripts or notebooks must be fully commented so I can trace every step later. Alongside the code, I want a short, hands-on learning component focused on the data-science aspects of Python: explanations of your ...

...straightforward: monitor and characterise movement patterns so I can spot changes over time and build a baseline for future comparisons. You will decide on the most appropriate combination of acceleration, gyroscopic and, if useful, magnetic-field signals; I’m open to any proven approach that highlights trends, periodicity or relevant statistics in the data. Feel free to work in Python (pandas, NumPy, SciPy, scikit-learn) or MATLAB—whichever you are fastest with—as long as the analysis can be rerun easily on new datasets. Deliverables • A well-commented script or notebook that loads my CSV/JSON log, processes it, and outputs key movement metrics. • Visualisations (plots or dashboards) that make the patterns easy to read at a glance. • A conc...

I have an Excel file from a medical study that compares a standard-care cohort with an intervention group. There are roughly ten specific questions I need to answer, and each hinges on pulling...that pairs the intervention results with the standard-care results for every variable involved in the ten questions. • The test statistics, p-values and confidence intervals where appropriate. • A short, plain-language write-up (bullet or paragraph form is fine) that directly answers each of the ten questions so I can drop it into a report. You may use Excel functions, R, Python (pandas, SciPy, statsmodels) or whichever tool you’re fastest with—just note the method so I can reproduce it later if needed. The raw data stay on my end; I’ll supply a de-identifi...

...pixel data, perform the K-Means algorithm, extract the medoid of each cluster, and visualise both the medoids and a sample of their nearest neighbours. Include a short quantitative evaluation (e.g., inertia, silhouette, or any metric you feel is appropriate for Euclidean K-Means on image data) and a brief discussion of cluster quality. Deliverables • A reproducible Jupyter notebook (Python, NumPy/SciPy, scikit-learn, Matplotlib or Seaborn) with clear, step-by-step code cells. • Inline comments explaining key choices—especially how the medoids were picked from the K-Means results. • Final visualisations: a grid of medoid images and at least one plot that illustrates cluster separation. Acceptance criteria: the notebook must run end-to-end without manual ...

...over the line to journal-ready quality. The dataset is already cleaned and the preliminary models are coded; what I need now is a skilled collaborator who can tighten both the analytics and the manuscript itself. Here’s what the assignment looks like from my side: • Review and, where necessary, refine the existing inferential tests and mathematical modelling logic in the Python notebook (NumPy, SciPy, pandas, statsmodels are already in use). • Generate publication-grade visualisations and summary tables that meet typical journal standards. • Suggest and implement any additional, defensible statistical checks that strengthen the results section. • Edit and format the manuscript (LaTeX or Word—your choice) so that it aligns with target-journa...

...recitation matches the reference recitation. Minimum Scoring Logic • Extract MFCC features for both audios • Apply time-alignment (Dynamic Time Warping / DTW) • Generate similarity index or score (0–100%) Scoring Output • Display percentage match or similarity value • Optional: o Per-segment accuracy o Highlight mismatched sections Recommended Technology • Python + Librosa (audio analysis) • NumPy / SciPy (mathematical processing) • Scoring engine exposed via API (FastAPI / Flask / Node bridge) 3. Advanced Features (Highly Valuable) A. Dual Waveform Overlay • Show both waveforms simultaneously: o Reference audio o Student audio • Used for: o Pitch alignment o Tempo comparison o Visual correction B. Error Highlighting •...

...be implemented, tested, and benchmarked in both Python and MATLAB. The core work involves translating existing mathematical formulations into efficient, well-documented code, validating the results against reference datasets, and profiling performance so the same algorithm behaves consistently across the two languages. Much of the heavy lifting will rely on standard scientific stacks—NumPy, SciPy, pandas, and matplotlib on the Python side, with equivalent functions and toolboxes in MATLAB—so fluency with these libraries is essential. I will provide the underlying equations, sample input files, and baseline outputs; the task is to turn those pieces into clean, reusable functions or class modules, then verify accuracy to at least six significant figures and demonstra...

...implemented in my project files. The freelancer will be responsible for installing and configuring: 1. Required Environment Python 3.10+ Jupyter Notebook Pip & virtual environment GPU support (CUDA & cuDNN) if my laptop supports it Required Python packages including: pandas, numpy scikit-learn lightgbm seaborn, matplotlib tensorflow / keras (for CNN) torch + torch_geometric (for GNN) scipy pillow tqdm networkx utilities 2. Dataset Setup Please prepare the following datasets on my laptop: • EMBER2018 (CSV features) Used for LightGBM. • Malimg Dataset Folder structure with malware family images for CNN classification. • LAMDA Dataset CSV with features and labels for GNN and drift analysis. I will provide the datasets if needed. 3....

...run thousands of virtual patient courses, reproducing the variability seen in real-world data. • Output easily adjustable parameters so I can test alternative dosing schedules or drug combinations without rewriting code. • Provide concise documentation and a short walkthrough video or notebook demonstrating how to run the model and interpret its outputs. Python with libraries such as NumPy, SciPy, or SimPy is preferred for transparency, but I am open to other languages if you can justify the benefits. Code must be annotated, reproducible, and accompanied by a brief validation summary comparing simulated versus reported incidence rates. Upon delivery, I will confirm accuracy by checking that extracted data match the source articles and that simulation outputs align...

...happiness, neutral, disgust Extracts heart rate (BPM) from facial color variations using rPPG Calculates a combined stress level (Low / Medium / High) Displays live output on the screen Optionally triggers alerts or stores stress logs for analysis Technologies Used: Python OpenCV MediaPipe / DeepFace (for facial emotion detection) rPPG algorithms (CHROM / POS) for heart-rate estimation NumPy / SciPy Streamlit or Flask (optional front-end UI) Key Features: Real-time Facial Emotion Recognition Contactless heart-rate estimation Combined stress scoring algorithm High accuracy due to multi-parameter analysis Can be deployed as a web app, mobile app, or desktop application Suitable for corporate wellness, student monitoring, driver stress detection, healthcare applications, and acade...

I have a mature Python codebase—well over 500 lines—that performs scientific computation with heavy use of NumPy and SciPy for array operations, linear algebra, and numerical routines. I need a clean, function-for-function Fortran translation that matches the current behavior and accuracy. Here is what I’m looking for: • A readable Fortran 90/95 (or newer) implementation that mirrors the existing module structure and logic. • Replacement of NumPy/SciPy calls with native or well-established Fortran libraries (e.g., LAPACK, BLAS) while preserving performance. • Clear in-code comments explaining any algorithmic changes and the mapping from Python constructs to Fortran equivalents. • A simple makefile or CMake configuration plus compil...

...workflow: first cleaning and preprocessing the data, then running solid statistical analyses, and finally producing publication-ready visualisations. The raw data arrive as CSVs exported from laboratory equipment. I need you to: • Build robust preprocessing scripts (pandas / NumPy) to handle missing values, outliers and unit conversions. • Apply appropriate statistical tests and models with SciPy or statsmodels and summarise the findings in plain language. • Create concise, attractive plots—think matplotlib or seaborn—that I can drop straight into a report or slide deck. Deliverables • Well-commented Python scripts or Jupyter notebooks covering each step • A brief markdown or PDF report that explains methods, key statistics and interpr...

...variables per case. My priority is to uncover how these variables relate to one another, so I need a thorough correlation study—Pearson where assumptions hold, and an alternative (e.g., Spearman) when they do not—alongside the classic descriptive stats: mean, standard deviation, and any other summary measures you believe add insight. You are free to work in the environment you prefer—Python (pandas, SciPy, seaborn), R (tidyverse, ggplot2), SPSS, Stata, or any other tool that lets you produce reproducible results. Visual clarity matters, so please include readable tables and at least one visual overview such as a correlation heat-map or scatter-matrix. Deliverables • A short, well-structured report (PDF, HTML, or Jupyter Notebook) that walks through ...

...(min⁻¹) Enhancement (%) TiO₂/ZnO/WO₃ (1:1:1) 0.035–0.050 +60–80% vs. single-oxide pH Adjustment Factors (f_pH): • pH 3: 0.65 • pH 5: 0.85 • pH 6.5: 1.00 (reference) • pH 8: 0.90 • pH 10: 0.70 • pH 11: 0.60 Dosage Adjustment (f_light): • 0.5 g/L: 0.67 • 1.0 g/L: 1.00 (reference) • 1.5 g/L: 0.95 • 2.0 g/L: 0.90 Technical Requirements Programming Language & Environment • Preferred: Python 3.8+ (SciPy, NumPy, Pandas, Matplotlib) • Alternative: MATLAB R2023a or later • Operating System: Windows, macOS, or Linux compatible • Dependencies: Clearly documented; use standard, open-source libraries Functionality Requirements 1. ✓ Read input parameters from CSV or Excel files 2. ✓ Solve ODE (pseudo-f...

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...factors from the metadata, and convert everything into true engineering units. • Write one tidy CSV per channel with time stamps and calibrated amplitudes. • Add a compact feature-extraction module that calculates RMS, peak values, and FFT-based spectral bins so I can validate data quality quickly. • Deliver well-commented, Python-only source code that relies on NumPy, struct, and pandas (plus SciPy for the FFT if you prefer). The code should run end-to-end on the sample files I supply and leave room to plug in additional sensors later. Acceptance check A run of against my sample archive should (1) generate correctly named CSVs, (2) produce a separate summarising RMS, peak, and chosen FFT bins, and (3) complete without manual tweaks on a vanilla Python 3...

I have a complete Python implementation of a grid-based path-planning system that blends classic A* search with my own Modified-Reward Q-Learning (MR-QLearning) for autonomous drone navigation in search-and-rescue scenarios. Alongside the code, I provide a report th...All original results are either replicated within an agreed tolerance or the divergence is fully justified. • Every algorithmic step in the code can be mapped unambiguously to the report’s equations and pseudocode. • Suggestions are feasible, technically sound, and improve either accuracy or speed. The project is self-contained—just install the usual Python scientific stack (NumPy, jupyter, SciPy, Matplotlib, perhaps gym-like utilities) and run the scripts. I will share the source code and rep...

...hiding beneath the surface. Clear visual storytelling matters to me, so please translate the findings into companion charts—bar, line, scatter, or whatever best communicates the insights. While charts are my stated preference, I’m happy to consider tables or even lightweight dashboards if they help tell the story effectively. Your workflow can be Excel-centric, Python-based (Pandas, NumPy, SciPy, matplotlib, seaborn), or a mix; use whichever tools let you work fastest without sacrificing accuracy. Throughout the project I expect prompt, transparent communication so we can course-correct quickly if needed. Deliverables: • A cleaned, well-documented business dataset (Excel or CSV) • An inferential statistics write-up with methodology and results &bu...

...Python script that taps into a live camera feed, detects standard geometric shapes—think rectangles, squares, circles, and triangles—and instantly returns their length and width on-screen. A small overlay showing the measurements in real time is ideal so I can validate results at a glance. OpenCV will almost certainly be the backbone for frame capture and contour detection; NumPy (and optionally SciPy) will handle any needed calculations. If you prefer another computer-vision library that still gives reliable results, that’s fine as long as setup stays straightforward on Windows and Linux. Deliverables • Well-documented .py file(s) that open the webcam, identify each geometric shape, and output its length and width in user-friendly units • A short ...

I have a complete Python implementation of a grid-based path-planning system that blends classic A* search with my own Modified-Reward Q-Learning (MR-QLearning) for autonomous drone navigation in search-and-rescue scenarios. Alongside the code, I provide a report tha...original results are either replicated within an agreed tolerance or the divergence is fully justified. • Every algorithmic step in the code can be mapped unambiguously to the report’s equations and pseudocode. • Suggestions are feasible, technically sound, and improve either accuracy or speed. The project is self-contained—just install the usual Python scientific stack (NumPy, jupyter, SciPy, Matplotlib, perhaps gym-like utilities) and run the scripts. I will share the source code and rep...

...• Input: JPEG still images only (the module will later sit behind a live camera, but for now we work with files). • Shapes: both regular and irregular polygons. • Accuracy: results must be physically realistic; target ≤2 % error against ground-truth measurements in my validation set. • Code style: modular, readable, and ready to be dropped into an existing Python + OpenCV pipeline (NumPy/SciPy welcomed). Deliverables 1. Fully commented source code organised into importable classes or functions. 2. A short README explaining the maths, calibration steps, and integration points. 3. A minimal test harness that ingests a folder of JPEGs and emits a JSON or CSV report matching the fields above. 4. Sample output on at least three differently lit o...

I need to pull numerical fields from one or more databases, clean and structure them, then run a solid statistical analysis that answers a set of...to design a repeatable extraction routine (SQL queries or an ETL script), handle any required data-quality checks, and produce clear statistical insights. Ideally the workflow ends with two concrete artefacts: • A tidy dataset (CSV or XLSX) produced directly from the database pull. • A short statistical report—tables, charts, and a concise narrative of the findings—created in Python (pandas, SciPy, seaborn) or R if you prefer. Everything should be reproducible: I want the code, queries, and brief instructions so I can rerun the process as new data arrives. Accuracy and transparency matter more to me than fancy d...

...a full quality-improvement pipeline. The work involves: • Cleaning & validation – handle missing values, catch outliers, verify ranges • Transformation & normalization – apply whatever scaling or encoding is most appropriate for later modelling or dashboard use • Standardisation & final QA – be sure the final table is tidy, well-labelled, and export-ready Feel free to use pandas, NumPy, SciPy or any other mainstream libraries; a short, well-commented notebook or script that reproduces each step will be ideal. Once the processing is complete, I need a concise report (around 4 pages including figures and pictures fine) that zeroes in on the findings and results—summary statistics, notable corrections, before-and-after sn...

...hinges on a chain of integral equations, so I want a careful audit of the derivations, assumptions, and boundary conditions. • Once the math is solid and the figures reproduce without surprises, help me reorganize the material into a narrative suitable for peer review—introduction, methods, results, discussion, references, and any supplemental Jupyter notebooks. Typical toolset Python (NumPy, SciPy, matplotlib, PyCBC), a bit of MATLAB for legacy scripts, LaTeX for the manuscript, and Git for version control. If you prefer Mathematica or Julia for integral work, that’s fine as long as you document the workflow. Acceptance criteria 1. Reproducible verification notebook showing the SCG signal extraction on LIGO open data with clear comments. 2. Annotated derivatio...

...correction Multi-user vocal alignment (CQT, DTW, NCC) AI-driven mixing and mastering (LUFS normalization, stereo correlation, psychoacoustic QC) Implement neural models using PyTorch, torchaudio, and librosa Integrate with FastAPI or NodeJS backends and deploy to AWS Optimize latency, throughput, and reliability for large-scale audio data Required Skills Strong experience with Python, NumPy, SciPy, PyTorch, and librosa Solid understanding of Digital Signal Processing (FFT, spectral analysis, filters, time/pitch algorithms) Familiarity with AI-based audio systems (voice isolation, dereverb, tuning, enhancement) Experience with AWS, Docker, and CI/CD (Optional) Experience integrating with React, Flutter, or Web Audio API Engagement Details Remote work with GSK Producti...

I ...record entered accurately—no copy-paste errors, clear field mapping, and proper data typing. Once the data is safely in the database, I want a concise statistical analysis that highlights key trends, variance, and any significant correlations you spot. I’m comfortable if you work in MySQL, PostgreSQL, or an equivalent relational setup, and I’d like the analysis scripted in either Python (pandas, SciPy) or R—whichever you’re fastest with. Deliverables: • Populated and validated database (dump or direct access). • Cleaned dataset exported as .csv. • A short statistical report (PDF or Jupyter/R Markdown) including code, summary tables, and visualizations. Accuracy and clarity are my top priorities, so please build in basic data-q...

...architecture TECHNOLOGIES USED: Hardware: KEYENCE LJ-X8080 (laser profilometer), LJ-X8000A controller, CNC system Language: Python 3.x Libraries: NumPy, OpenCV, Open3D, socket (TCP/IP), serial, scipy Protocols: Ethernet communication with KEYENCE (TCP ports 24691/24692) Data Format: 3D point clouds, laser profiles (3200 points/line) Typical Dimensions: Surfaces up to 2m × 2m, micrometric resolution REQUIRED SKILLS: - Advanced Python expertise (5+ years professional experience) - 3D point cloud processing (Open3D, PCL, or equivalent) - Industrial hardware communication (TCP/IP sockets, serial protocols) - NumPy, scipy for matrix calculations and data processing - Experience with industrial measurement equipment (KEYENCE, vision systems) - Alignment and registratio...

...(Sub-project 1) will lead us to Phase 2, and then to Phase 3, etc. I will be hiring separately for each phase. If you do an excellent job, you’ll have a strong chance of continuing with me into the next phase. Before placing a bid, please review the list of requirements carefully to avoid wasting your time or mine. Required Skills: Advanced Python programming and data handling (pandas, NumPy, SciPy), API integration, and version control with Git. Experience with LLM fine-tuning (Hugging Face, SFT, LoRA, DPO), bias and fairness evaluation in NLP, Elastic Weight Consolidation, safety/alignment testing, and GPU/compute optimization. Knowledge of cybersecurity concepts, multilingual data handling, and annotation/validation processes. Proficiency in statistical analysis, evalu...

...and fixes. Outputs: New Excel sheet + printable PDF; include timestamps and stats (e.g., avg. deviation). User Interface: Simple GUI (Tkinter or Streamlit preferred) with file upload, datum input, "Check Compliance," and "Generate Report" buttons. Support batch processing; modular design for easy tolerance updates. Technical Preferences Core Stack: Python 3.x with pandas (data handling), numpy/scipy (calculations), matplotlib/plotly (visuals), openpyxl/xlsxwriter (Excel I/O). Alternatives: Excel VBA if Python constraints arise (must scale to 10,000+ points). Outputs: Static Excel/PDF or interactive HTML (via Streamlit/Dash). Edge Cases: Uneven grids, noisy data (filters), unit conversions (mm/inches). Deliverables Full source code (zipped or GitHub repo), f...

...files arrive as CSVs and include thousands of records and dozens of columns, so efficient data-wrangling skills are essential. The scope covers: • Cleaning and validating each column (handling missing values, outliers, and inconsistent formats). • Generating core descriptive measures—mean, median, mode, standard deviation, variance, percentiles, correlation matrix—using pandas, NumPy, or SciPy. • Producing a concise, clearly commented Python script (or Jupyter notebook) that walks through every step from import to final summary tables. • Delivering a short markdown or PDF report explaining methods, key findings, and any notable insights. Reproducibility matters: package versions should be pinned, and the code should run on a standard ...

Deep Learning & Modeling: This includes training the Conditional Progressive Transformer, performing CSLR pre-training, implementing baseline models (DA...streams, extraction of Non-Manual Features (NMFs), and simulation of occlusions. Required skills are OpenCV, MediaPipe (or similar), and Python programming. Statistics & Analysis: Tasks include computing FID and pose-level MSE for the generated corpus, conducting significance testing, calculating bootstrapped confidence intervals, and performing ablation studies. Required skills are Python or R, SciPy or Statsmodels, and general data analysis. Linguistics: This involves designing gloss sequences and evaluating the linguistic fidelity of the generated sequences. Required skills include statistical language modeling and se...

...study of low-pass filtering. The document should: • Explain the theory behind low-pass filters, including cutoff frequency selection and expected magnitude-phase behavior. • Walk through the design of at least one digital low-pass filter (e.g., FIR with windowing or IIR Butterworth), justifying each design choice. • Present step-by-step implementation and testing in MATLAB, Python (NumPy/SciPy), or Octave—whichever you are most comfortable with. • Include plots of the input signal, frequency response, and filtered output, together with brief commentary on the results. • Summarize key observations and potential improvements in a concise conclusion section. Please keep the writing academic yet accessible, reference any formulas you use, ...